演化多目标优化的锥束分解机理与高效算法研究

项目名称： 演化多目标优化的锥束分解机理与高效算法研究

项目编号： No.61203310

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 自动化学科

项目作者： 应伟勤

作者单位： 华南理工大学

项目金额： 25万元

中文摘要： 分解和超体积是当前多目标演化算法的两个主流发展方向，基于分解的算法虽具有较高的计算效率，但所求解集质量易受Pareto前沿形状的影响；超体积是已知的唯一一个关于Pareto占优严格单调的解集评价指标，但其极高计算成本阻碍了在算法中充分利用这一指标。本项目从锥束划分目标空间的独特几何视角结合分解与超体积的优点研究提高多目标演化算法计算效率和解集质量的方法。研究内容包括：引入理想点、观察向量等将目标空间划分为一系列锥形子区域，为无序无结构的种群赋予有序的锥形邻域结构；基于锥束划分探索更完善的锥束分解机理，在分解的同时给每个子问题分配一个独占的锥形子区域，进一步提高基于分解的算法的效率；通过引入锥超体积指标在锥束分解的同时成功融合超体积信息，使算法既能利用到超体积的理想数学特性引导种群搜索高质量的解集，又可通过分解避免高成本的超体积计算。本项目的研究将显著提高多目标演化算法的计算效率和解集质量。

中文关键词： 演化算法；多目标优化；分解；超体积；计算效率

英文摘要： Two major development trends of multiobjective evolutiaonary algorithms (MOEAs) are hypervolume and decomposition. MOEAs based on decomposition attain much higher computational efficiencies, but the qualities of the approximate Pareto sets achieved by these algorithms are vulnerable to the facts such as the geometrical shapes of the Pareto frontiers (PFs). The hypervolume indicator is the only single set quality measure that is known to be strictly monotonic with regard to Pareto dominance. However, the high computational effort required for hypervolume calculation has so far prevented to fully exploit the potential of this indicator. This project will study the mechanisms and methods to improve both the computaitonal efficiency and the solution set quality of MOEAs by combining the advantages of decomposition and hypervolume from the unique geometrical perspective of conical-beam partition of the objective space. This project includes the following research contents. First the objective space will be divided into a series of conical subrgions by introducing the concepts such as the utopian point and observation vectors and the disorder and unstructured population will be transformed into the orderly one with the conical neighborhood structures. Then the conical-beam partition of the objective space is used to e

英文关键词： evolutionary algorithm；multiobjective optimization；decomposition；hypervolume；computational efficiency